File size: 87,128 Bytes
7b505e6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 |
---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sparse-encoder
- sparse
- splade
- generated_from_trainer
- dataset_size:99000
- loss:SpladeLoss
- loss:SparseMultipleNegativesRankingLoss
- loss:FlopsLoss
base_model: distilbert/distilbert-base-uncased
widget:
- text: How do I know if a girl likes me at school?
- text: What are some five star hotel in Jaipur?
- text: Is it normal to fantasize your wife having sex with another man?
- text: What is the Sahara, and how do the average temperatures there compare to the
ones in the Simpson Desert?
- text: What are Hillary Clinton's most recognized accomplishments while Secretary
of State?
datasets:
- sentence-transformers/quora-duplicates
pipeline_tag: feature-extraction
library_name: sentence-transformers
metrics:
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- cosine_mcc
- dot_accuracy
- dot_accuracy_threshold
- dot_f1
- dot_f1_threshold
- dot_precision
- dot_recall
- dot_ap
- dot_mcc
- euclidean_accuracy
- euclidean_accuracy_threshold
- euclidean_f1
- euclidean_f1_threshold
- euclidean_precision
- euclidean_recall
- euclidean_ap
- euclidean_mcc
- manhattan_accuracy
- manhattan_accuracy_threshold
- manhattan_f1
- manhattan_f1_threshold
- manhattan_precision
- manhattan_recall
- manhattan_ap
- manhattan_mcc
- max_accuracy
- max_accuracy_threshold
- max_f1
- max_f1_threshold
- max_precision
- max_recall
- max_ap
- max_mcc
- active_dims
- sparsity_ratio
- dot_accuracy@1
- dot_accuracy@3
- dot_accuracy@5
- dot_accuracy@10
- dot_precision@1
- dot_precision@3
- dot_precision@5
- dot_precision@10
- dot_recall@1
- dot_recall@3
- dot_recall@5
- dot_recall@10
- dot_ndcg@10
- dot_mrr@10
- dot_map@100
- query_active_dims
- query_sparsity_ratio
- corpus_active_dims
- corpus_sparsity_ratio
co2_eq_emissions:
emissions: 1.4164940270091377
energy_consumed: 0.02527693261851813
source: codecarbon
training_type: fine-tuning
on_cloud: false
cpu_model: AMD Ryzen 9 6900HX with Radeon Graphics
ram_total_size: 30.6114501953125
hours_used: 0.222
hardware_used: 1 x NVIDIA GeForce RTX 3070 Ti Laptop GPU
model-index:
- name: splade-distilbert-base-uncased trained on Quora Duplicates Questions
results:
- task:
type: sparse-binary-classification
name: Sparse Binary Classification
dataset:
name: quora duplicates dev
type: quora_duplicates_dev
metrics:
- type: cosine_accuracy
value: 0.758
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.8166326284408569
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.6792899408284023
name: Cosine F1
- type: cosine_f1_threshold
value: 0.5695896148681641
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.5487571701720841
name: Cosine Precision
- type: cosine_recall
value: 0.8913043478260869
name: Cosine Recall
- type: cosine_ap
value: 0.6887627674706448
name: Cosine Ap
- type: cosine_mcc
value: 0.508171027288805
name: Cosine Mcc
- type: dot_accuracy
value: 0.765
name: Dot Accuracy
- type: dot_accuracy_threshold
value: 51.6699104309082
name: Dot Accuracy Threshold
- type: dot_f1
value: 0.6762028608582575
name: Dot F1
- type: dot_f1_threshold
value: 46.524925231933594
name: Dot F1 Threshold
- type: dot_precision
value: 0.5816554809843401
name: Dot Precision
- type: dot_recall
value: 0.8074534161490683
name: Dot Recall
- type: dot_ap
value: 0.6335823489360819
name: Dot Ap
- type: dot_mcc
value: 0.4996270089694481
name: Dot Mcc
- type: euclidean_accuracy
value: 0.677
name: Euclidean Accuracy
- type: euclidean_accuracy_threshold
value: -14.272356986999512
name: Euclidean Accuracy Threshold
- type: euclidean_f1
value: 0.48599545798637395
name: Euclidean F1
- type: euclidean_f1_threshold
value: -0.6444530487060547
name: Euclidean F1 Threshold
- type: euclidean_precision
value: 0.3213213213213213
name: Euclidean Precision
- type: euclidean_recall
value: 0.9968944099378882
name: Euclidean Recall
- type: euclidean_ap
value: 0.2032823056922341
name: Euclidean Ap
- type: euclidean_mcc
value: -0.04590966956831287
name: Euclidean Mcc
- type: manhattan_accuracy
value: 0.677
name: Manhattan Accuracy
- type: manhattan_accuracy_threshold
value: -161.77682495117188
name: Manhattan Accuracy Threshold
- type: manhattan_f1
value: 0.48599545798637395
name: Manhattan F1
- type: manhattan_f1_threshold
value: -3.0494537353515625
name: Manhattan F1 Threshold
- type: manhattan_precision
value: 0.3213213213213213
name: Manhattan Precision
- type: manhattan_recall
value: 0.9968944099378882
name: Manhattan Recall
- type: manhattan_ap
value: 0.20444314945561334
name: Manhattan Ap
- type: manhattan_mcc
value: -0.04590966956831287
name: Manhattan Mcc
- type: max_accuracy
value: 0.765
name: Max Accuracy
- type: max_accuracy_threshold
value: 51.6699104309082
name: Max Accuracy Threshold
- type: max_f1
value: 0.6792899408284023
name: Max F1
- type: max_f1_threshold
value: 46.524925231933594
name: Max F1 Threshold
- type: max_precision
value: 0.5816554809843401
name: Max Precision
- type: max_recall
value: 0.9968944099378882
name: Max Recall
- type: max_ap
value: 0.6887627674706448
name: Max Ap
- type: max_mcc
value: 0.508171027288805
name: Max Mcc
- type: active_dims
value: 78.32280731201172
name: Active Dims
- type: sparsity_ratio
value: 0.9974338900690646
name: Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoMSMARCO
type: NanoMSMARCO
metrics:
- type: dot_accuracy@1
value: 0.22
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.42
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.52
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.76
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.22
name: Dot Precision@1
- type: dot_precision@3
value: 0.13999999999999999
name: Dot Precision@3
- type: dot_precision@5
value: 0.10400000000000001
name: Dot Precision@5
- type: dot_precision@10
value: 0.07600000000000001
name: Dot Precision@10
- type: dot_recall@1
value: 0.22
name: Dot Recall@1
- type: dot_recall@3
value: 0.42
name: Dot Recall@3
- type: dot_recall@5
value: 0.52
name: Dot Recall@5
- type: dot_recall@10
value: 0.76
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.45321847177875746
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.3601269841269841
name: Dot Mrr@10
- type: dot_map@100
value: 0.37334906504034243
name: Dot Map@100
- type: query_active_dims
value: 74.76000213623047
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9975506191554868
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 103.06523895263672
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9966232475279261
name: Corpus Sparsity Ratio
- type: dot_accuracy@1
value: 0.22
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.42
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.52
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.76
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.22
name: Dot Precision@1
- type: dot_precision@3
value: 0.13999999999999999
name: Dot Precision@3
- type: dot_precision@5
value: 0.10400000000000001
name: Dot Precision@5
- type: dot_precision@10
value: 0.07600000000000001
name: Dot Precision@10
- type: dot_recall@1
value: 0.22
name: Dot Recall@1
- type: dot_recall@3
value: 0.42
name: Dot Recall@3
- type: dot_recall@5
value: 0.52
name: Dot Recall@5
- type: dot_recall@10
value: 0.76
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.45321847177875746
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.3601269841269841
name: Dot Mrr@10
- type: dot_map@100
value: 0.37334906504034243
name: Dot Map@100
- type: query_active_dims
value: 74.76000213623047
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9975506191554868
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 103.06523895263672
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9966232475279261
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: dot_accuracy@1
value: 0.38
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.54
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.62
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.62
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.38
name: Dot Precision@1
- type: dot_precision@3
value: 0.18
name: Dot Precision@3
- type: dot_precision@5
value: 0.12400000000000003
name: Dot Precision@5
- type: dot_precision@10
value: 0.06400000000000002
name: Dot Precision@10
- type: dot_recall@1
value: 0.36
name: Dot Recall@1
- type: dot_recall@3
value: 0.52
name: Dot Recall@3
- type: dot_recall@5
value: 0.6
name: Dot Recall@5
- type: dot_recall@10
value: 0.61
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.4828377104499333
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.4536666666666666
name: Dot Mrr@10
- type: dot_map@100
value: 0.445384784044708
name: Dot Map@100
- type: query_active_dims
value: 74.73999786376953
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9975512745605213
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 141.31478881835938
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9953700678586476
name: Corpus Sparsity Ratio
- type: dot_accuracy@1
value: 0.38
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.54
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.62
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.62
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.38
name: Dot Precision@1
- type: dot_precision@3
value: 0.18
name: Dot Precision@3
- type: dot_precision@5
value: 0.12400000000000003
name: Dot Precision@5
- type: dot_precision@10
value: 0.06400000000000002
name: Dot Precision@10
- type: dot_recall@1
value: 0.36
name: Dot Recall@1
- type: dot_recall@3
value: 0.52
name: Dot Recall@3
- type: dot_recall@5
value: 0.6
name: Dot Recall@5
- type: dot_recall@10
value: 0.61
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.4828377104499333
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.4536666666666666
name: Dot Mrr@10
- type: dot_map@100
value: 0.445384784044708
name: Dot Map@100
- type: query_active_dims
value: 74.73999786376953
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9975512745605213
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 141.31478881835938
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9953700678586476
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNFCorpus
type: NanoNFCorpus
metrics:
- type: dot_accuracy@1
value: 0.34
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.5
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.54
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.58
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.34
name: Dot Precision@1
- type: dot_precision@3
value: 0.30666666666666664
name: Dot Precision@3
- type: dot_precision@5
value: 0.26
name: Dot Precision@5
- type: dot_precision@10
value: 0.198
name: Dot Precision@10
- type: dot_recall@1
value: 0.011597172822497613
name: Dot Recall@1
- type: dot_recall@3
value: 0.06058581579610722
name: Dot Recall@3
- type: dot_recall@5
value: 0.08260772201759854
name: Dot Recall@5
- type: dot_recall@10
value: 0.09800124609193644
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.2466972614666078
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.42200000000000004
name: Dot Mrr@10
- type: dot_map@100
value: 0.09401937795309984
name: Dot Map@100
- type: query_active_dims
value: 79.69999694824219
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9973887688569477
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 202.17269897460938
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9933761647672298
name: Corpus Sparsity Ratio
- type: dot_accuracy@1
value: 0.34
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.5
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.54
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.58
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.34
name: Dot Precision@1
- type: dot_precision@3
value: 0.30666666666666664
name: Dot Precision@3
- type: dot_precision@5
value: 0.26
name: Dot Precision@5
- type: dot_precision@10
value: 0.198
name: Dot Precision@10
- type: dot_recall@1
value: 0.011597172822497613
name: Dot Recall@1
- type: dot_recall@3
value: 0.06058581579610722
name: Dot Recall@3
- type: dot_recall@5
value: 0.08260772201759854
name: Dot Recall@5
- type: dot_recall@10
value: 0.09800124609193644
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.2466972614666078
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.42200000000000004
name: Dot Mrr@10
- type: dot_map@100
value: 0.09401937795309984
name: Dot Map@100
- type: query_active_dims
value: 79.69999694824219
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9973887688569477
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 202.17269897460938
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9933761647672298
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoQuoraRetrieval
type: NanoQuoraRetrieval
metrics:
- type: dot_accuracy@1
value: 0.94
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.98
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.98
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.98
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.94
name: Dot Precision@1
- type: dot_precision@3
value: 0.3933333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.24799999999999997
name: Dot Precision@5
- type: dot_precision@10
value: 0.13199999999999998
name: Dot Precision@10
- type: dot_recall@1
value: 0.8173333333333332
name: Dot Recall@1
- type: dot_recall@3
value: 0.9279999999999999
name: Dot Recall@3
- type: dot_recall@5
value: 0.946
name: Dot Recall@5
- type: dot_recall@10
value: 0.97
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.9467235239993945
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.96
name: Dot Mrr@10
- type: dot_map@100
value: 0.9290737327188939
name: Dot Map@100
- type: query_active_dims
value: 76.58000183105469
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9974909900455063
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 77.59056854248047
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9974578805929336
name: Corpus Sparsity Ratio
- type: dot_accuracy@1
value: 0.94
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.98
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.98
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.98
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.94
name: Dot Precision@1
- type: dot_precision@3
value: 0.3933333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.24799999999999997
name: Dot Precision@5
- type: dot_precision@10
value: 0.13199999999999998
name: Dot Precision@10
- type: dot_recall@1
value: 0.8173333333333332
name: Dot Recall@1
- type: dot_recall@3
value: 0.9279999999999999
name: Dot Recall@3
- type: dot_recall@5
value: 0.946
name: Dot Recall@5
- type: dot_recall@10
value: 0.97
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.9467235239993945
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.96
name: Dot Mrr@10
- type: dot_map@100
value: 0.9290737327188939
name: Dot Map@100
- type: query_active_dims
value: 76.58000183105469
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9974909900455063
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 77.59056854248047
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9974578805929336
name: Corpus Sparsity Ratio
- task:
type: sparse-nano-beir
name: Sparse Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: dot_accuracy@1
value: 0.47
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.61
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.665
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.735
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.47
name: Dot Precision@1
- type: dot_precision@3
value: 0.255
name: Dot Precision@3
- type: dot_precision@5
value: 0.184
name: Dot Precision@5
- type: dot_precision@10
value: 0.1175
name: Dot Precision@10
- type: dot_recall@1
value: 0.3522326265389577
name: Dot Recall@1
- type: dot_recall@3
value: 0.4821464539490268
name: Dot Recall@3
- type: dot_recall@5
value: 0.5371519305043997
name: Dot Recall@5
- type: dot_recall@10
value: 0.6095003115229841
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5323692419236733
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5489484126984127
name: Dot Mrr@10
- type: dot_map@100
value: 0.46045673993926106
name: Dot Map@100
- type: query_active_dims
value: 76.44499969482422
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9974954131546155
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 122.79780664247188
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9959767444255792
name: Corpus Sparsity Ratio
- type: dot_accuracy@1
value: 0.4359811616954475
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.6088540031397174
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.6659026687598116
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.7383987441130299
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.4359811616954475
name: Dot Precision@1
- type: dot_precision@3
value: 0.2725170068027211
name: Dot Precision@3
- type: dot_precision@5
value: 0.2089481946624804
name: Dot Precision@5
- type: dot_precision@10
value: 0.14605965463108322
name: Dot Precision@10
- type: dot_recall@1
value: 0.2532746332292894
name: Dot Recall@1
- type: dot_recall@3
value: 0.3813452238818861
name: Dot Recall@3
- type: dot_recall@5
value: 0.4363867898661836
name: Dot Recall@5
- type: dot_recall@10
value: 0.5099503000039356
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.4684519639817077
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5328029827315542
name: Dot Mrr@10
- type: dot_map@100
value: 0.39738635557561647
name: Dot Map@100
- type: query_active_dims
value: 90.39137197532713
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9970384846348428
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 152.36685474307478
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9950079662295042
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoClimateFEVER
type: NanoClimateFEVER
metrics:
- type: dot_accuracy@1
value: 0.18
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.32
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.4
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.48
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.18
name: Dot Precision@1
- type: dot_precision@3
value: 0.10666666666666666
name: Dot Precision@3
- type: dot_precision@5
value: 0.08400000000000002
name: Dot Precision@5
- type: dot_precision@10
value: 0.054000000000000006
name: Dot Precision@10
- type: dot_recall@1
value: 0.085
name: Dot Recall@1
- type: dot_recall@3
value: 0.14666666666666667
name: Dot Recall@3
- type: dot_recall@5
value: 0.17833333333333332
name: Dot Recall@5
- type: dot_recall@10
value: 0.215
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.1845115403570178
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.2674126984126984
name: Dot Mrr@10
- type: dot_map@100
value: 0.1475834110231865
name: Dot Map@100
- type: query_active_dims
value: 89.86000061035156
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9970558940891701
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 221.75527954101562
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.992734575730915
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoDBPedia
type: NanoDBPedia
metrics:
- type: dot_accuracy@1
value: 0.6
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.84
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.84
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.92
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.6
name: Dot Precision@1
- type: dot_precision@3
value: 0.5266666666666666
name: Dot Precision@3
- type: dot_precision@5
value: 0.456
name: Dot Precision@5
- type: dot_precision@10
value: 0.4220000000000001
name: Dot Precision@10
- type: dot_recall@1
value: 0.04570544957623723
name: Dot Recall@1
- type: dot_recall@3
value: 0.15367137863132574
name: Dot Recall@3
- type: dot_recall@5
value: 0.1908008582920462
name: Dot Recall@5
- type: dot_recall@10
value: 0.293554014064817
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5070720730882787
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.7147222222222225
name: Dot Mrr@10
- type: dot_map@100
value: 0.3906658166774757
name: Dot Map@100
- type: query_active_dims
value: 69.5199966430664
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.997722298779796
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 135.93350219726562
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9955463763122578
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoFEVER
type: NanoFEVER
metrics:
- type: dot_accuracy@1
value: 0.58
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.76
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.8
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.86
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.58
name: Dot Precision@1
- type: dot_precision@3
value: 0.26666666666666666
name: Dot Precision@3
- type: dot_precision@5
value: 0.16799999999999998
name: Dot Precision@5
- type: dot_precision@10
value: 0.09
name: Dot Precision@10
- type: dot_recall@1
value: 0.5466666666666666
name: Dot Recall@1
- type: dot_recall@3
value: 0.7466666666666667
name: Dot Recall@3
- type: dot_recall@5
value: 0.7866666666666667
name: Dot Recall@5
- type: dot_recall@10
value: 0.8466666666666667
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.7069849294263234
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.6765000000000001
name: Dot Mrr@10
- type: dot_map@100
value: 0.6651380090497737
name: Dot Map@100
- type: query_active_dims
value: 89.87999725341797
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9970552389340994
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 221.215576171875
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9927522581688004
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoFiQA2018
type: NanoFiQA2018
metrics:
- type: dot_accuracy@1
value: 0.28
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.42
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.46
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.5
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.28
name: Dot Precision@1
- type: dot_precision@3
value: 0.18
name: Dot Precision@3
- type: dot_precision@5
value: 0.136
name: Dot Precision@5
- type: dot_precision@10
value: 0.08399999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.14183333333333334
name: Dot Recall@1
- type: dot_recall@3
value: 0.24288888888888888
name: Dot Recall@3
- type: dot_recall@5
value: 0.27715873015873016
name: Dot Recall@5
- type: dot_recall@10
value: 0.3288730158730159
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.28813286680239514
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.3561904761904763
name: Dot Mrr@10
- type: dot_map@100
value: 0.2415362537997973
name: Dot Map@100
- type: query_active_dims
value: 82.86000061035156
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9972852368583202
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 130.93699645996094
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9957100780925245
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoHotpotQA
type: NanoHotpotQA
metrics:
- type: dot_accuracy@1
value: 0.78
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.84
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.92
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.98
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.78
name: Dot Precision@1
- type: dot_precision@3
value: 0.3733333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.28400000000000003
name: Dot Precision@5
- type: dot_precision@10
value: 0.16
name: Dot Precision@10
- type: dot_recall@1
value: 0.39
name: Dot Recall@1
- type: dot_recall@3
value: 0.56
name: Dot Recall@3
- type: dot_recall@5
value: 0.71
name: Dot Recall@5
- type: dot_recall@10
value: 0.8
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.7143331285788386
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.8361904761904762
name: Dot Mrr@10
- type: dot_map@100
value: 0.6181181734895289
name: Dot Map@100
- type: query_active_dims
value: 91.9800033569336
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9969864359033833
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 152.01571655273438
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9950194706587794
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoSCIDOCS
type: NanoSCIDOCS
metrics:
- type: dot_accuracy@1
value: 0.36
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.58
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.68
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.76
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.36
name: Dot Precision@1
- type: dot_precision@3
value: 0.2733333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.21199999999999997
name: Dot Precision@5
- type: dot_precision@10
value: 0.15199999999999997
name: Dot Precision@10
- type: dot_recall@1
value: 0.07566666666666666
name: Dot Recall@1
- type: dot_recall@3
value: 0.16966666666666666
name: Dot Recall@3
- type: dot_recall@5
value: 0.21766666666666665
name: Dot Recall@5
- type: dot_recall@10
value: 0.31066666666666665
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.30291194083231554
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.4943888888888889
name: Dot Mrr@10
- type: dot_map@100
value: 0.21666464487074008
name: Dot Map@100
- type: query_active_dims
value: 94.30000305175781
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.996910425167035
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 199.64630126953125
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9934589377737524
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoArguAna
type: NanoArguAna
metrics:
- type: dot_accuracy@1
value: 0.1
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.34
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.42
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.44
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.1
name: Dot Precision@1
- type: dot_precision@3
value: 0.1133333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.084
name: Dot Precision@5
- type: dot_precision@10
value: 0.044000000000000004
name: Dot Precision@10
- type: dot_recall@1
value: 0.1
name: Dot Recall@1
- type: dot_recall@3
value: 0.34
name: Dot Recall@3
- type: dot_recall@5
value: 0.42
name: Dot Recall@5
- type: dot_recall@10
value: 0.44
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.2781554838544819
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.22466666666666665
name: Dot Mrr@10
- type: dot_map@100
value: 0.2332757160696607
name: Dot Map@100
- type: query_active_dims
value: 189.10000610351562
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9938044687077021
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 164.03329467773438
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9946257357093985
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoSciFact
type: NanoSciFact
metrics:
- type: dot_accuracy@1
value: 0.52
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.62
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.64
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.76
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.52
name: Dot Precision@1
- type: dot_precision@3
value: 0.21333333333333332
name: Dot Precision@3
- type: dot_precision@5
value: 0.14
name: Dot Precision@5
- type: dot_precision@10
value: 0.08399999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.475
name: Dot Recall@1
- type: dot_recall@3
value: 0.58
name: Dot Recall@3
- type: dot_recall@5
value: 0.615
name: Dot Recall@5
- type: dot_recall@10
value: 0.74
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6020710919940331
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5799047619047619
name: Dot Mrr@10
- type: dot_map@100
value: 0.5551340236204781
name: Dot Map@100
- type: query_active_dims
value: 82.45999908447266
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9972983422094073
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 194.24940490722656
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9936357576532591
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoTouche2020
type: NanoTouche2020
metrics:
- type: dot_accuracy@1
value: 0.3877551020408163
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.7551020408163265
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.8367346938775511
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.9591836734693877
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.3877551020408163
name: Dot Precision@1
- type: dot_precision@3
value: 0.4693877551020407
name: Dot Precision@3
- type: dot_precision@5
value: 0.4163265306122449
name: Dot Precision@5
- type: dot_precision@10
value: 0.33877551020408164
name: Dot Precision@10
- type: dot_recall@1
value: 0.02376760958202688
name: Dot Recall@1
- type: dot_recall@3
value: 0.08934182714819683
name: Dot Recall@3
- type: dot_recall@5
value: 0.12879429112534482
name: Dot Recall@5
- type: dot_recall@10
value: 0.21659229068805946
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.37622550913382224
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5806689342403627
name: Dot Mrr@10
- type: dot_map@100
value: 0.2560796141253303
name: Dot Map@100
- type: query_active_dims
value: 79.12245178222656
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9974076911151881
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 135.00782775878906
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9955767044178366
name: Corpus Sparsity Ratio
---
# splade-distilbert-base-uncased trained on Quora Duplicates Questions
This is a [SPLADE Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) dataset using the [sentence-transformers](https://www.SBERT.net) library. It maps sentences & paragraphs to a 30522-dimensional sparse vector space and can be used for semantic search and sparse retrieval.
## Model Details
### Model Description
- **Model Type:** SPLADE Sparse Encoder
- **Base model:** [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) <!-- at revision 12040accade4e8a0f71eabdb258fecc2e7e948be -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 30522 dimensions
- **Similarity Function:** Dot Product
- **Training Dataset:**
- [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates)
- **Language:** en
- **License:** apache-2.0
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Documentation:** [Sparse Encoder Documentation](https://www.sbert.net/docs/sparse_encoder/usage/usage.html)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder)
### Full Model Architecture
```
SparseEncoder(
(0): MLMTransformer({'max_seq_length': 256, 'do_lower_case': False}) with MLMTransformer model: DistilBertForMaskedLM
(1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SparseEncoder
# Download from the 🤗 Hub
model = SparseEncoder("arthurbresnu/splade-distilbert-base-uncased-quora-duplicates")
# Run inference
sentences = [
'What accomplishments did Hillary Clinton achieve during her time as Secretary of State?',
"What are Hillary Clinton's most recognized accomplishments while Secretary of State?",
'What are Hillary Clinton’s qualifications to be President?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# (3, 30522)
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Sparse Binary Classification
* Dataset: `quora_duplicates_dev`
* Evaluated with [<code>SparseBinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseBinaryClassificationEvaluator)
| Metric | Value |
|:-----------------------------|:-----------|
| cosine_accuracy | 0.758 |
| cosine_accuracy_threshold | 0.8166 |
| cosine_f1 | 0.6793 |
| cosine_f1_threshold | 0.5696 |
| cosine_precision | 0.5488 |
| cosine_recall | 0.8913 |
| cosine_ap | 0.6888 |
| cosine_mcc | 0.5082 |
| dot_accuracy | 0.765 |
| dot_accuracy_threshold | 51.6699 |
| dot_f1 | 0.6762 |
| dot_f1_threshold | 46.5249 |
| dot_precision | 0.5817 |
| dot_recall | 0.8075 |
| dot_ap | 0.6336 |
| dot_mcc | 0.4996 |
| euclidean_accuracy | 0.677 |
| euclidean_accuracy_threshold | -14.2724 |
| euclidean_f1 | 0.486 |
| euclidean_f1_threshold | -0.6445 |
| euclidean_precision | 0.3213 |
| euclidean_recall | 0.9969 |
| euclidean_ap | 0.2033 |
| euclidean_mcc | -0.0459 |
| manhattan_accuracy | 0.677 |
| manhattan_accuracy_threshold | -161.7768 |
| manhattan_f1 | 0.486 |
| manhattan_f1_threshold | -3.0495 |
| manhattan_precision | 0.3213 |
| manhattan_recall | 0.9969 |
| manhattan_ap | 0.2044 |
| manhattan_mcc | -0.0459 |
| max_accuracy | 0.765 |
| max_accuracy_threshold | 51.6699 |
| max_f1 | 0.6793 |
| max_f1_threshold | 46.5249 |
| max_precision | 0.5817 |
| max_recall | 0.9969 |
| **max_ap** | **0.6888** |
| max_mcc | 0.5082 |
| active_dims | 78.3228 |
| sparsity_ratio | 0.9974 |
#### Sparse Information Retrieval
* Datasets: `NanoMSMARCO`, `NanoNQ`, `NanoNFCorpus`, `NanoQuoraRetrieval`, `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020`
* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator)
| Metric | NanoMSMARCO | NanoNQ | NanoNFCorpus | NanoQuoraRetrieval | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 |
|:----------------------|:------------|:-----------|:-------------|:-------------------|:-----------------|:------------|:----------|:-------------|:-------------|:------------|:------------|:------------|:---------------|
| dot_accuracy@1 | 0.22 | 0.38 | 0.34 | 0.94 | 0.18 | 0.6 | 0.58 | 0.28 | 0.78 | 0.36 | 0.1 | 0.52 | 0.3878 |
| dot_accuracy@3 | 0.42 | 0.54 | 0.5 | 0.98 | 0.32 | 0.84 | 0.76 | 0.42 | 0.84 | 0.58 | 0.34 | 0.62 | 0.7551 |
| dot_accuracy@5 | 0.52 | 0.62 | 0.54 | 0.98 | 0.4 | 0.84 | 0.8 | 0.46 | 0.92 | 0.68 | 0.42 | 0.64 | 0.8367 |
| dot_accuracy@10 | 0.76 | 0.62 | 0.58 | 0.98 | 0.48 | 0.92 | 0.86 | 0.5 | 0.98 | 0.76 | 0.44 | 0.76 | 0.9592 |
| dot_precision@1 | 0.22 | 0.38 | 0.34 | 0.94 | 0.18 | 0.6 | 0.58 | 0.28 | 0.78 | 0.36 | 0.1 | 0.52 | 0.3878 |
| dot_precision@3 | 0.14 | 0.18 | 0.3067 | 0.3933 | 0.1067 | 0.5267 | 0.2667 | 0.18 | 0.3733 | 0.2733 | 0.1133 | 0.2133 | 0.4694 |
| dot_precision@5 | 0.104 | 0.124 | 0.26 | 0.248 | 0.084 | 0.456 | 0.168 | 0.136 | 0.284 | 0.212 | 0.084 | 0.14 | 0.4163 |
| dot_precision@10 | 0.076 | 0.064 | 0.198 | 0.132 | 0.054 | 0.422 | 0.09 | 0.084 | 0.16 | 0.152 | 0.044 | 0.084 | 0.3388 |
| dot_recall@1 | 0.22 | 0.36 | 0.0116 | 0.8173 | 0.085 | 0.0457 | 0.5467 | 0.1418 | 0.39 | 0.0757 | 0.1 | 0.475 | 0.0238 |
| dot_recall@3 | 0.42 | 0.52 | 0.0606 | 0.928 | 0.1467 | 0.1537 | 0.7467 | 0.2429 | 0.56 | 0.1697 | 0.34 | 0.58 | 0.0893 |
| dot_recall@5 | 0.52 | 0.6 | 0.0826 | 0.946 | 0.1783 | 0.1908 | 0.7867 | 0.2772 | 0.71 | 0.2177 | 0.42 | 0.615 | 0.1288 |
| dot_recall@10 | 0.76 | 0.61 | 0.098 | 0.97 | 0.215 | 0.2936 | 0.8467 | 0.3289 | 0.8 | 0.3107 | 0.44 | 0.74 | 0.2166 |
| **dot_ndcg@10** | **0.4532** | **0.4828** | **0.2467** | **0.9467** | **0.1845** | **0.5071** | **0.707** | **0.2881** | **0.7143** | **0.3029** | **0.2782** | **0.6021** | **0.3762** |
| dot_mrr@10 | 0.3601 | 0.4537 | 0.422 | 0.96 | 0.2674 | 0.7147 | 0.6765 | 0.3562 | 0.8362 | 0.4944 | 0.2247 | 0.5799 | 0.5807 |
| dot_map@100 | 0.3733 | 0.4454 | 0.094 | 0.9291 | 0.1476 | 0.3907 | 0.6651 | 0.2415 | 0.6181 | 0.2167 | 0.2333 | 0.5551 | 0.2561 |
| query_active_dims | 74.76 | 74.74 | 79.7 | 76.58 | 89.86 | 69.52 | 89.88 | 82.86 | 91.98 | 94.3 | 189.1 | 82.46 | 79.1225 |
| query_sparsity_ratio | 0.9976 | 0.9976 | 0.9974 | 0.9975 | 0.9971 | 0.9977 | 0.9971 | 0.9973 | 0.997 | 0.9969 | 0.9938 | 0.9973 | 0.9974 |
| corpus_active_dims | 103.0652 | 141.3148 | 202.1727 | 77.5906 | 221.7553 | 135.9335 | 221.2156 | 130.937 | 152.0157 | 199.6463 | 164.0333 | 194.2494 | 135.0078 |
| corpus_sparsity_ratio | 0.9966 | 0.9954 | 0.9934 | 0.9975 | 0.9927 | 0.9955 | 0.9928 | 0.9957 | 0.995 | 0.9935 | 0.9946 | 0.9936 | 0.9956 |
#### Sparse Nano BEIR
* Dataset: `NanoBEIR_mean`
* Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters:
```json
{
"dataset_names": [
"msmarco",
"nq",
"nfcorpus",
"quoraretrieval"
]
}
```
| Metric | Value |
|:----------------------|:-----------|
| dot_accuracy@1 | 0.47 |
| dot_accuracy@3 | 0.61 |
| dot_accuracy@5 | 0.665 |
| dot_accuracy@10 | 0.735 |
| dot_precision@1 | 0.47 |
| dot_precision@3 | 0.255 |
| dot_precision@5 | 0.184 |
| dot_precision@10 | 0.1175 |
| dot_recall@1 | 0.3522 |
| dot_recall@3 | 0.4821 |
| dot_recall@5 | 0.5372 |
| dot_recall@10 | 0.6095 |
| **dot_ndcg@10** | **0.5324** |
| dot_mrr@10 | 0.5489 |
| dot_map@100 | 0.4605 |
| query_active_dims | 76.445 |
| query_sparsity_ratio | 0.9975 |
| corpus_active_dims | 122.7978 |
| corpus_sparsity_ratio | 0.996 |
#### Sparse Nano BEIR
* Dataset: `NanoBEIR_mean`
* Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters:
```json
{
"dataset_names": [
"climatefever",
"dbpedia",
"fever",
"fiqa2018",
"hotpotqa",
"msmarco",
"nfcorpus",
"nq",
"quoraretrieval",
"scidocs",
"arguana",
"scifact",
"touche2020"
]
}
```
| Metric | Value |
|:----------------------|:-----------|
| dot_accuracy@1 | 0.436 |
| dot_accuracy@3 | 0.6089 |
| dot_accuracy@5 | 0.6659 |
| dot_accuracy@10 | 0.7384 |
| dot_precision@1 | 0.436 |
| dot_precision@3 | 0.2725 |
| dot_precision@5 | 0.2089 |
| dot_precision@10 | 0.1461 |
| dot_recall@1 | 0.2533 |
| dot_recall@3 | 0.3813 |
| dot_recall@5 | 0.4364 |
| dot_recall@10 | 0.51 |
| **dot_ndcg@10** | **0.4685** |
| dot_mrr@10 | 0.5328 |
| dot_map@100 | 0.3974 |
| query_active_dims | 90.3914 |
| query_sparsity_ratio | 0.997 |
| corpus_active_dims | 152.3669 |
| corpus_sparsity_ratio | 0.995 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### quora-duplicates
* Dataset: [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb)
* Size: 99,000 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 14.1 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.83 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.21 tokens</li><li>max: 75 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:----------------------------------------------------------------------|:---------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What are the best GMAT coaching institutes in Delhi NCR?</code> | <code>Which are the best GMAT coaching institutes in Delhi/NCR?</code> | <code>What are the best GMAT coaching institutes in Delhi-Noida Area?</code> |
| <code>Is a third world war coming?</code> | <code>Is World War 3 more imminent than expected?</code> | <code>Since the UN is unable to control terrorism and groups like ISIS, al-Qaeda and countries that promote terrorism (even though it consumed those countries), can we assume that the world is heading towards World War III?</code> |
| <code>Should I build iOS or Android apps first?</code> | <code>Should people choose Android or iOS first to build their App?</code> | <code>How much more effort is it to build your app on both iOS and Android?</code> |
* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
```json
{
"loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')",
"lambda_corpus": 3e-05,
"lambda_query": 5e-05
}
```
### Evaluation Dataset
#### quora-duplicates
* Dataset: [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb)
* Size: 1,000 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 14.05 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.14 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.56 tokens</li><li>max: 60 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:-------------------------------------------------------------------|:------------------------------------------------------------|:-----------------------------------------------------------------|
| <code>What happens if we use petrol in diesel vehicles?</code> | <code>Why can't we use petrol in diesel?</code> | <code>Why are diesel engines noisier than petrol engines?</code> |
| <code>Why is Saltwater taffy candy imported in Switzerland?</code> | <code>Why is Saltwater taffy candy imported in Laos?</code> | <code>Is salt a consumer product?</code> |
| <code>Which is your favourite film in 2016?</code> | <code>What movie is the best movie of 2016?</code> | <code>What will the best movie of 2017 be?</code> |
* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
```json
{
"loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')",
"lambda_corpus": 3e-05,
"lambda_query": 5e-05
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 12
- `per_device_eval_batch_size`: 12
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `bf16`: True
- `load_best_model_at_end`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 12
- `per_device_eval_batch_size`: 12
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `tp_size`: 0
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | quora_duplicates_dev_max_ap | NanoMSMARCO_dot_ndcg@10 | NanoNQ_dot_ndcg@10 | NanoNFCorpus_dot_ndcg@10 | NanoQuoraRetrieval_dot_ndcg@10 | NanoBEIR_mean_dot_ndcg@10 | NanoClimateFEVER_dot_ndcg@10 | NanoDBPedia_dot_ndcg@10 | NanoFEVER_dot_ndcg@10 | NanoFiQA2018_dot_ndcg@10 | NanoHotpotQA_dot_ndcg@10 | NanoSCIDOCS_dot_ndcg@10 | NanoArguAna_dot_ndcg@10 | NanoSciFact_dot_ndcg@10 | NanoTouche2020_dot_ndcg@10 |
|:-------:|:--------:|:-------------:|:---------------:|:---------------------------:|:-----------------------:|:------------------:|:------------------------:|:------------------------------:|:-------------------------:|:----------------------------:|:-----------------------:|:---------------------:|:------------------------:|:------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:--------------------------:|
| 0.0242 | 200 | 8.3389 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0485 | 400 | 0.4397 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0727 | 600 | 0.3737 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0970 | 800 | 0.2666 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1212 | 1000 | 0.288 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1455 | 1200 | 0.1977 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1697 | 1400 | 0.2707 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1939 | 1600 | 0.1951 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2 | 1650 | - | 0.1669 | 0.6472 | 0.3052 | 0.2793 | 0.1711 | 0.9281 | 0.4209 | - | - | - | - | - | - | - | - | - |
| 0.2182 | 1800 | 0.2178 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2424 | 2000 | 0.2174 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2667 | 2200 | 0.1832 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2909 | 2400 | 0.1879 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3152 | 2600 | 0.1723 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3394 | 2800 | 0.1543 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3636 | 3000 | 0.1559 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3879 | 3200 | 0.1575 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4 | 3300 | - | 0.1149 | 0.6749 | 0.3894 | 0.4467 | 0.2360 | 0.9292 | 0.5003 | - | - | - | - | - | - | - | - | - |
| 0.4121 | 3400 | 0.1395 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4364 | 3600 | 0.1596 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4606 | 3800 | 0.1595 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4848 | 4000 | 0.1211 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5091 | 4200 | 0.1163 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5333 | 4400 | 0.1182 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5576 | 4600 | 0.1337 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5818 | 4800 | 0.1362 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6 | 4950 | - | 0.1001 | 0.6802 | 0.4093 | 0.4269 | 0.2341 | 0.9365 | 0.5017 | - | - | - | - | - | - | - | - | - |
| 0.6061 | 5000 | 0.1112 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6303 | 5200 | 0.1064 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6545 | 5400 | 0.119 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6788 | 5600 | 0.1077 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7030 | 5800 | 0.1398 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7273 | 6000 | 0.09 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7515 | 6200 | 0.0903 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7758 | 6400 | 0.1082 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8 | 6600 | 0.1122 | 0.0901 | 0.6941 | 0.4451 | 0.4757 | 0.2542 | 0.9411 | 0.5290 | - | - | - | - | - | - | - | - | - |
| 0.8242 | 6800 | 0.0708 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8485 | 7000 | 0.1291 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8727 | 7200 | 0.1165 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8970 | 7400 | 0.0735 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9212 | 7600 | 0.0775 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9455 | 7800 | 0.0945 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9697 | 8000 | 0.0912 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9939 | 8200 | 0.104 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| **1.0** | **8250** | **-** | **0.0686** | **0.6888** | **0.4532** | **0.4828** | **0.2467** | **0.9467** | **0.5324** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** |
| -1 | -1 | - | - | - | 0.4532 | 0.4828 | 0.2467 | 0.9467 | 0.4685 | 0.1845 | 0.5071 | 0.7070 | 0.2881 | 0.7143 | 0.3029 | 0.2782 | 0.6021 | 0.3762 |
* The bold row denotes the saved checkpoint.
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.025 kWh
- **Carbon Emitted**: 0.001 kg of CO2
- **Hours Used**: 0.222 hours
### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 3070 Ti Laptop GPU
- **CPU Model**: AMD Ryzen 9 6900HX with Radeon Graphics
- **RAM Size**: 30.61 GB
### Framework Versions
- Python: 3.12.9
- Sentence Transformers: 4.2.0.dev0
- Transformers: 4.50.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### SpladeLoss
```bibtex
@misc{formal2022distillationhardnegativesampling,
title={From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective},
author={Thibault Formal and Carlos Lassance and Benjamin Piwowarski and Stéphane Clinchant},
year={2022},
eprint={2205.04733},
archivePrefix={arXiv},
primaryClass={cs.IR},
url={https://arxiv.org/abs/2205.04733},
}
```
#### SparseMultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
#### FlopsLoss
```bibtex
@article{paria2020minimizing,
title={Minimizing flops to learn efficient sparse representations},
author={Paria, Biswajit and Yeh, Chih-Kuan and Yen, Ian EH and Xu, Ning and Ravikumar, Pradeep and P{'o}czos, Barnab{'a}s},
journal={arXiv preprint arXiv:2004.05665},
year={2020}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |